TY  - CONF
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011964903&doi=10.1109%2fICIAS.2016.7824047&partnerID=40&md5=1a2347b7f3c5484caaf2e644dd258024
A1  - Mathur, N.
A1  - Asirvadam, V.S.
A1  - Dass, S.C.
A1  - Gill, B.S.
SN  - 9781509008452
PB  - Institute of Electrical and Electronics Engineers Inc.
Y1  - 2017///
KW  - Forecasting; Geographic information systems; Maximum principle; Visualization
KW  -  Centroid models; Controlled process; Dengue; Distribution models; EM algorithms; Expectation-maximization algorithms; K-means; Prediction model
KW  -  Clustering algorithms
TI  - Visualization of dengue incidences using expectation maximization (EM) algorithm
ID  - scholars8981
N1  - cited By 3; Conference of 6th International Conference on Intelligent and Advanced Systems, ICIAS 2016 ; Conference Date: 15 August 2016 Through 17 August 2016; Conference Code:125970
N2  - The aim of this study was to use the geographical information system (GIS) to visualize the dengue incidences on a weekly basis in Selangor, Malaysia. Along with the prediction modeling on data using centroid model and distribution model based on K-means and Expectation Maximization (EM) algorithms respectively. The results show that weekly hotspot were mainly concentrated in the central part of Petaling district of Selangor. R-GIS(R software) and clustering algorithm were used for year 2014 with several weeks to develop the relation between the visualization and prediction of reported incidences. The results are validated for a small region (Petaling district of Selangor state) in Malaysia and they showed vulnerability hotspot in visualizing the dengue incidences. Thus, the proposed method is able to localize the nature of dengue incidence which can further be utilized for vector disease controlled process. © 2016 IEEE.
AV  - none
ER  -